CS 482
Computer Vision

Time/Location: Monday, Wednesday 1:30-2:45pm, Art and Design Building 2026
Instructor: Dr. Jana Kosecka
Office: 4444, Research II
email: kosecka@gmu.edu
Office hours:  2-3pm Tuesday
Course website http://cs.gmu.edu/~kosecka/cs482/

This course will cover essentials of computer vision. We will learn basic principles of image formation, image processing algorithms and different algorithms for 3D reconstruction and recognition from single or multiple images (video). Apllications to 3D modelling, video analysis, video surveillance, object recognition and vision based control will be discussed.  

This course is of interest to anyone seeking to process images or camera information, or to acquire a general background in issues related to real-world perception, image processing, object and scene recognition and multi-view geometry

Schedule, Homeworks, Handouts

Grading Homeworks (about every 2 weeks) 40% Midterm: 30% Final exam: 30%
Prerequisites linear algebra, calculus
Lecture Materials Lecture slides, lecture notes provided by instructor

Recommended Textbooks

[1] Invitation to 3D Vision: From Images to Geometric Models: Y. Ma, S. Soatto, J. Kosecka and S. Sastry web site
[2] Computer Vision: A Modern Approach: D. Forsythe and J. Ponce, Prentice-Hall, 2003
[3] Computer Vision: Algorithms and Applications. R. Szeliski, 2010, Springer online version of the book
[4] Image Processing, Analysis, and Machine Vision. Sonka, Hlavac, and Boyle. Thomson.
[5] Computer Vision. Ballard and Brown web site

Required Software

Matlab, OpenCV. Homeworks will require using Matlab and OpenCV. You can buy a student version of Matlab in Johnson center or use it remotely from ITE labs. OpenCV is an C/C++ open source computer vision library.

Course Outcomes

Basic knowledge of image formation process
Basic knowledge of image processing techniques for color and gray level images: edge detection, corner detection, segmentation
Basics of video processing, motion computation and 3D vision and geometry
Ability to implement basic vision algorithms in Matlab and use OpenCV (open source computer vision library)
Ability to apply the appropriate technique to a problem, write a project report and present the results in class.